

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class      lowerCamelCase_      :
	_lowercase		:   int
	_lowercase		:   int






class      lowerCamelCase_      :




	def __init__(     self						:     Tuple					,      __A						:     int							):
		__A :			list[list[Edge]]		    =			[[] for _ in range(__A							)]
		__A :			Optional[int]		    =			size




	def __getitem__(     self						:     Optional[Any]					,      __A						:     int							):
		return iter(self._graph[vertex]							)




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return self._size




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     int					,      __A						:     int					,      __A						:     int							):
		if weight not in (0, 1):
			raise ValueError("""Edge weight must be either 0 or 1."""							)

		if to_vertex < 0 or to_vertex >= self.size:
			raise ValueError("""Vertex indexes must be in [0; size)."""							)

		self._graph[from_vertex].append(Edge(__A					,      __A							)							)




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     int					,      __A						:     int							):
		__A :			Any		    =			deque([start_vertex]							)
		__A :			list[int | None]		    =			[None] * self.size
		__A :			Any		    =			0

		while queue:
			__A :			Dict		    =			queue.popleft()
			__A :			int		    =			distances[current_vertex]
			if current_distance is None:
				continue

			for edge in self[current_vertex]:
				__A :			Union[str, Any]		    =			current_distance + edge.weight
				__A :			int		    =			distances[edge.destination_vertex]
				if (
				    isinstance(__A					,      __A							)
				    and new_distance >= dest_vertex_distance
				):
					continue
				__A :			str		    =			new_distance
				if edge.weight == 0:
					queue.appendleft(edge.destination_vertex							)
				else:
					queue.append(edge.destination_vertex							)

		if distances[finish_vertex] is None:
			raise ValueError("""No path from start_vertex to finish_vertex."""							)

		return distances[finish_vertex]


if __name__ == "__main__":
			import doctest

			doctest.testmod()








from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


